def test_nb2():
seed = 1234
rng = np.random.default_rng(seed)
n_obs, n_var, n_nnz, rsq, k = 2000, 20, 4, 0.9**2, 4.0
X = exact_rmvnorm(np.eye(n_var), n=n_obs, seed=seed)
beta = np.zeros(n_var)
bv = np.array([-1.0, -0.5, 0.5, 1.0])
bvals = np.tile(bv, n_nnz // len(bv))
if n_nnz % len(bv) > 0:
bvals = np.concatenate([bvals, bv[:n_nnz % len(bv)]])
beta[:n_nnz] = bvals
eta = X.dot(beta) / np.sqrt(np.sum(beta**2))
lpred = rng.normal(eta, scale=np.sqrt(eta.var() * (1.0 - rsq) / rsq))
mu = np.exp(lpred)
var = mu + k * mu**2
n = -mu**2 / (mu - var)
p = mu / var
y = rng.negative_binomial(n=n, p=p)
xcols = [f"x{i}" for i in range(1, n_var + 1)]
data = pd.DataFrame(X, columns=xcols)
data['y'] = y
formula = "y~1+" + "+".join(xcols)
model = NegativeBinomial(formula=formula, data=data)
params_init = model.params.copy() + 0.01
model.fit()
params = model.params.copy()
theta = np.array([
0.13049303, -0.64878454, -0.30956394, 0.2903795, 0.58677555,
-0.03022705, 0.03989469, 0.01182953, -0.00498391, 0.00788808,
-0.04198716, -0.00162041, 0.01523861, -0.00401566, -0.02547227,
-0.07309814, -0.05574522, 0.00938691, -0.0034148, -0.01254539,
-0.05221309, 1.41286364
])
g_num1 = fo_fc_cd(model.loglike, params_init)
g_ana1 = model.gradient(params_init)
g_num2 = fo_fc_cd(model.loglike, params)
g_ana2 = model.gradient(params)
H_num1 = so_gc_cd(model.gradient, params_init)
H_ana1 = model.hessian(params_init)
H_num2 = so_gc_cd(model.gradient, params)
H_ana2 = model.hessian(params)
assert (np.allclose(model.params, theta))
assert (np.allclose(g_num1, g_ana1))
assert (np.allclose(g_num2, g_ana2, atol=1e-4))
assert (np.allclose(H_num1, H_ana1))
assert (np.allclose(H_num2, H_ana2))
assert (model.opt_full.success)
def test_holzingercfa():
data = pd.read_csv("https://vincentarelbundock.github.io/Rdatasets/csv/sem/HS.data.csv", index_col=0)
df = data.loc[:, ["visual", "cubes", "flags", "paragrap", "sentence",
"wordm", "addition", "counting", "straight"]]
df = df / np.array([6.,4.0, 8., 3., 4., 7., 23., 20., 36.])
Lambda = np.zeros((9, 3))
Lambda[0:3, 0] = 1.0
Lambda[3:6, 1] = 1.0
Lambda[6:9, 2] = 1.0
Lambda = pd.DataFrame(Lambda, index=df.columns, columns=['visual', 'textual', 'speed'])
Beta = pd.DataFrame(np.zeros((3, 3)), index=Lambda.columns, columns=Lambda.columns)
Phi=Beta+np.eye(3)+0.05
Psi = pd.DataFrame(np.eye(9), index=df.columns, columns=df.columns)
model = SEM(Lambda, Beta, Phi, Psi, data=df)
model.fit()
theta = np.array([0.55372 , 0.729526, 1.113068, 0.926117, 1.180358, 1.083565,
0.809095, 0.408174, 0.261583, 0.979517, 0.173783, 0.383061,
0.549275, 1.133711, 0.844258, 0.371148, 0.446243, 0.356234,
0.796578, 0.488285, 0.567506])
assert(np.allclose(model.theta, theta))
assert(model.opt.success)
assert(np.abs(model.opt.grad).max()<1e-5)
assert(np.allclose(model.gradient(theta+0.1), fo_fc_cd(model.loglike, theta+0.1)))
assert(np.allclose(model.hessian(theta), so_gc_cd(model.gradient, theta)))
def test_gauls():
n_obs = 20000
df = pd.DataFrame(np.zeros((n_obs, 4)), columns=['x0', 'x1', 'x2', 'y'])
df['x0'] = rng.choice(np.arange(5), size=n_obs, p=np.ones(5)/5)
df['x1'] = rng.uniform(-1, 1, size=n_obs)
df['x2'] = rng.uniform(-1, 1, size=n_obs)
u0 = dummy(df['x0']).dot(np.array([-0.2, 0.2, -0.2, 0.2, 0.0]))
f1 = (3.0 * df['x1']**3 - 2.43 * df['x1'])
f2 = -(3.0 * df['x2']**3 - 2.43 * df['x2'])
f3 = (df['x2'] - 1.0) * (df['x2'] + 1.0)
eta = u0 + f1 + f2
mu = eta.copy()
tau = 1.0 / (np.exp(f3) + 0.01)
df['y'] = rng.normal(loc=mu, scale=tau)
model = GauLS("y~C(x0)+s(x1, kind='cr')+s(x2, kind='cr')", "y~1+s(x2, kind='cr')", df)
model.fit()
assert(model.opt.success==True)
assert((np.abs(model.opt.grad)<1e-6).all())
theta = np.array([4.235378, 4.165951, 7.181457])
assert(np.allclose(theta, model.theta))
eps = np.finfo(float).eps**(1/4)
grad_close = np.allclose(model.gradient(theta), fo_fc_cd(model.reml, theta), atol=eps, rtol=eps)
hess_close = np.allclose(model.hessian(theta), so_gc_cd(model.gradient, theta), atol=eps, rtol=eps)
assert(grad_close)
assert(hess_close)
def test_zip():
seed = 1234
rng = np.random.default_rng(seed)
X = exact_rmvnorm(vine_corr(3, 5, seed=seed), seed=seed)
Z = exact_rmvnorm(vine_corr(2, 5, seed=seed), seed=seed)
b, a = rng.normal(0.0, 0.5, size=(3)), rng.normal(size=(2))
u = np.exp(Z.dot(a))
prob = u / (1.0 + u)
ybin = rng.binomial(1, p=prob)
y = np.zeros(len(ybin), dtype=float)
mu = np.exp(X.dot(b))
q = rng.poisson(mu[ybin == 1])
y[ybin == 1] = q * 1.0
model = ZIP(X, y, Z)
model.fit(opt_kws=dict(verbose=3, gtol=1e-9, xtol=1e-200))
theta = np.array(
[-0.81434379, 0.02346997, 0.34179484, 0.00402158, -0.82916627])
g1 = fo_fc_cd(model.loglike, model.params * 0.98)
g2 = model.gradient(model.params * 0.98)
H1 = so_gc_cd(model.gradient, model.params * 0.98)
H2 = model.hessian(model.params * 0.98)
assert (np.allclose(g1, g2))
assert (np.allclose(H1, H2))
assert (np.allclose(model.params, theta))
def test_clm():
SEED = 1234
rng = np.random.default_rng(SEED)
n_obs, n_var, rsquared = 10_000, 8, 0.25
S = np.eye(n_var)
X = exact_rmvnorm(S, n=n_obs, seed=1234)
beta = np.zeros(n_var)
beta[np.arange(n_var // 2)] = rng.choice([-1., 1., -0.5, 0.5], n_var // 2)
var_names = [f"x{i}" for i in range(1, n_var + 1)]
eta = X.dot(beta)
eta_var = eta.var()
scale = np.sqrt((1.0 - rsquared) / rsquared * eta_var)
y = rng.normal(eta, scale=scale)
df = pd.DataFrame(X, columns=var_names)
df["y"] = pd.qcut(y, 7).codes
formula = "y~-1+" + "+".join(var_names)
model = CLM(frm=formula, data=df)
model.fit()
theta = np.array([
-2.08417224, -1.08288221, -0.34199706, 0.34199368, 1.08217316,
2.08327387, 0.37275823, 0.37544884, 0.3572407, 0.71165265, 0.0086888,
-0.00846944, 0.00975741, 0.01257564
])
assert (np.allclose(theta, model.params))
params_init, params = model.params_init.copy(), model.params.copy()
tol = np.finfo(float).eps**(1 / 3)
np.allclose(model.gradient(params_init),
fo_fc_cd(model.loglike, params_init))
np.allclose(model.gradient(params),
fo_fc_cd(model.loglike, params),
atol=tol)
np.allclose(model.hessian(params_init),
so_gc_cd(model.gradient, params_init))
np.allclose(model.hessian(params), so_gc_cd(model.gradient, params))
def test_nlsy_model():
vechS = [2.926, 1.390, 1.698, 1.628, 1.240, 0.592, 0.929,
0.659, 4.257, 2.781, 2.437, 0.789, 1.890, 1.278, 0.949,
4.536, 2.979, 0.903, 1.419, 1.900, 1.731, 5.605, 1.278, 1.004,
1.000, 2.420, 3.208, 1.706, 1.567, 0.988, 3.994, 1.654, 1.170,
3.583, 1.146, 3.649]
S = pd.DataFrame(invech(np.array(vechS)), columns=['anti1', 'anti2',
'anti3', 'anti4', 'dep1', 'dep2', 'dep3', 'dep4'])
S.index = S.columns
X = pd.DataFrame(exact_rmvnorm(S.values, 180, seed=123), columns=S.columns)
X += np.array([1.750, 1.928, 1.978, 2.322, 2.178, 2.489, 2.294, 2.222])
data = pd.DataFrame(X, columns=S.columns)
Lambda = pd.DataFrame(np.eye(8), index=data.columns, columns=data.columns)
Lambda = Lambda.iloc[[1, 2, 3, 5, 6, 7, 0, 4], [1, 2, 3, 5, 6, 7, 0, 4]]
Beta = pd.DataFrame([[0, 0, 0, 0, 0, 0, 1, 1],
[1, 0, 0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 1],
[1, 0, 0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
], index=Lambda.columns, columns=Lambda.columns)
Phi = pd.DataFrame([[1.0, 0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0, 0.1, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0, 0.1, 0.0, 0.0],
[0.1, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.1, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.1, 0.0, 0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.1],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1, 1.0],
], index=Lambda.columns, columns=Lambda.columns)
Psi = Lambda.copy()*0.0
data = data.loc[:, Lambda.index]
model = SEM(Lambda, Beta, Phi, Psi, data=data)
model.fit()
theta = np.array([ 0.62734 , 0.147299, 0.69399 , 0.318353, 0.058419, 0.344418,
-0.088914, 0.151027, 0.443465, -0.027558, 0.074533, 0.542448,
3.581777, 1.500315, 2.70847 , 1.001634, 3.626519, 1.3206 ,
3.084899, 2.825085, 2.924854, 2.926 , 1.24 , 3.208 ])
assert(np.allclose(model.theta, theta))
assert(model.opt.success)
assert(np.abs(model.opt.grad).max()<1e-5)
assert(np.allclose(model.gradient(theta+0.1), fo_fc_cd(model.loglike, theta+0.1)))
assert(np.allclose(model.hessian(theta), so_gc_cd(model.gradient, theta)))
def test_sem_bollen():
data_bollen = pd.read_csv("https://vincentarelbundock.github.io/Rdatasets/csv/sem/Bollen.csv", index_col=0)
data_bollen = data_bollen[['x1', 'x2', 'x3', 'y1', 'y2', 'y3', 'y4', 'y5', 'y6', 'y7', 'y8', ]]
L = np.array([[1., 0., 0.],
[1., 0., 0.],
[1., 0., 0.],
[0., 1., 0.],
[0., 1., 0.],
[0., 1., 0.],
[0., 1., 0.],
[0., 0., 1.],
[0., 0., 1.],
[0., 0., 1.],
[0., 0., 1.]])
B = np.array([[0., 0., 0.],
[1., 0., 0.],
[1., 1., 0.]])
Lambda1 = pd.DataFrame(L, index=data_bollen.columns, columns=['ind60', 'dem60', 'dem65'])
Beta1 = pd.DataFrame(B, index=Lambda1.columns, columns=Lambda1.columns)
Psi1 = pd.DataFrame(np.eye(Lambda1.shape[0]), index=Lambda1.index, columns=Lambda1.index)
off_diag = [['y1', 'y5'], ['y2', 'y4'], ['y3', 'y7'], ['y4', 'y8'],
['y6', 'y8'], ['y2', 'y6']]
for x, y in off_diag:
Psi1.loc[x, y] = Psi1.loc[y, x] = 0.05
Phi1 = pd.DataFrame(np.eye(Lambda1.shape[1]), index=Lambda1.columns,
columns=Lambda1.columns)
model = SEM(Lambda1, Beta1, Phi1, Psi1, data=data_bollen)
model.fit()
theta = np.array([2.18036773, 1.81851130, 1.25674650, 1.05771677, 1.26478654,
1.18569630, 1.27951218, 1.26594698, 1.48300054, 0.57233619,
0.83734481, 0.44843715, 3.95603311, 0.17248133, 0.08154935,
0.11980648, 0.46670258, 1.89139552, 0.62367107, 7.37286854,
1.31311258, 2.15286127, 5.06746210, 0.79496028, 3.14790480,
0.34822604, 2.35097047, 4.95396775, 1.35616712, 3.43137392,
3.25408501])
assert(np.allclose(model.theta, theta))
assert(model.opt.success)
assert(np.abs(model.opt.grad).max()<1e-5)
assert(np.allclose(model.gradient(theta+0.1), fo_fc_cd(model.loglike, theta+0.1)))
assert(np.allclose(model.hessian(theta), so_gc_cd(model.gradient, theta)))
def test_lmm():
rng = np.random.default_rng(123)
n_grp, n_per = 100, 100
formula = "y~1+x1+x2+x3+(1+x3|id1)"
model_dict = {}
model_dict["ginfo"] = dict(id1=dict(n_grp=n_grp, n_per=n_per))
model_dict["beta"] = np.array([0.0, 0.5, -1.0, 1.0])
model_dict["vcov"] = np.eye(3)
model_dict["mu"] = np.zeros(3)
model_dict["gcov"] = {"id1": invech(np.array([2.0, -1.0, 2.0]))}
model_dict["n_obs"] = n_grp * n_per
group_dict = dict(id1=np.repeat(np.arange(n_grp), n_per))
rfe, rre = 0.4, 0.4
msim = MixedModelSim(formula=formula,
model_dict=model_dict,
rng=rng,
group_dict=group_dict,
var_ratios=np.array([rfe, rre]))
var_ratios = np.array([msim.v_fe, msim.v_re, msim.v_rs])
var_ratios = var_ratios / np.sum(var_ratios)
assert (np.allclose(var_ratios, np.array([0.4, 0.4, 0.2])))
df = msim.df.copy()
df["y"], u = msim.simulate_response()
model = LMM(formula, data=df)
model.fit(opt_kws=dict(xtol=1e-16, gtol=1e-12))
assert (model.optimizer.success == True)
assert ((np.abs(model.optimizer.grad) < 1e-2).all())
theta = np.array([0.95418349, -0.36657794, 0.84136618, 1.12001821])
assert (np.allclose(model.theta, theta))
eps = np.finfo(float).eps**(1 / 4)
grad_close = np.allclose(model.gradient(theta),
fo_fc_cd(model.loglike, theta),
atol=eps,
rtol=eps)
hess_close = np.allclose(model.hessian(theta),
so_gc_cd(model.gradient, theta),
atol=eps,
rtol=eps)
assert (grad_close)
assert (hess_close)
def test_sem_med():
x = rng.normal(0, 1, size=1000)
x = (x - x.mean()) / x.std()
u = rng.normal(0, 1, size=1000)
u = (u - u.mean()) / u.std()
v = rng.normal(0, 1, size=1000)
v = (v - v.mean()) / v.std()
m = 0.5*x + u
y = 0.7*m + v
data_path = pd.DataFrame(np.vstack((x, m, y)).T, columns=['x', 'm', 'y'])
data_path = data_path - data_path.mean(axis=0)
Lambda = pd.DataFrame(np.eye(3), index=data_path.columns, columns=data_path.columns)
Beta = np.array([[0.0, 0.0, 0.0],
[1.0, 0.0, 0.0],
[1.0, 1.0, 0.0]])
Beta = pd.DataFrame(Beta, index=data_path.columns, columns=data_path.columns)
Phi = np.array([[1.0, 0.0, 0.0],
[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0]])
Phi = pd.DataFrame(Phi, index=data_path.columns, columns=data_path.columns)
Psi = np.array([[0.0, 0.0, 0.0],
[0.0, 1.0, 0.0],
[0.0, 0.0, 1.0]])
Psi = pd.DataFrame(Psi, index=data_path.columns, columns=data_path.columns)
model = SEM(Lambda, Beta, Phi, Psi, data=data_path)
model.fit(opt_kws=dict(options=dict(gtol=1e-20, xtol=1e-100)))
theta = np.array([0.52667623, 0.13011021, 0.40325746, 1.00000000, 0.99928838,
1.43672041])
assert(np.allclose(model.theta, theta))
assert(model.opt.success)
assert(np.abs(model.opt.grad).max()<1e-5)
assert(np.allclose(model.gradient(theta+0.1), fo_fc_cd(model.loglike, theta+0.1)))
assert(np.allclose(model.hessian(theta), so_gc_cd(model.gradient, theta)))
def test_betareg():
SEED = 1234
rng = np.random.default_rng(SEED)
n_obs = 10_000
X = exact_rmvnorm(np.eye(4) / 100, n=n_obs, seed=SEED)
Z = exact_rmvnorm(np.eye(2) / 100, n=n_obs, seed=SEED)
betam = np.array([4.0, 1.0, -1.0, -2.0])
betas = np.array([2.0, -2.0])
etam, etas = X.dot(betam) + 1.0, 2 + Z.dot(
betas) #np.tanh(Z.dot(betas))/2.0 + 2.4
mu, phi = LogitLink().inv_link(etam), LogLink().inv_link(etas)
a = mu * phi
b = (1.0 - mu) * phi
y = rng.beta(a, b)
xcols = [f"x{i}" for i in range(1, 4 + 1)]
zcols = [f"z{i}" for i in range(1, 2 + 1)]
data = pd.DataFrame(np.hstack((X, Z)), columns=xcols + zcols)
data["y"] = y
m_formula = "y~1+" + "+".join(xcols)
s_formula = "y~1+" + "+".join(zcols)
model = BetaReg(m_formula=m_formula, s_formula=s_formula, data=data)
model.fit()
theta = np.array([
0.99819859, 3.92262116, 1.02091902, -0.98526682, -1.9795528,
1.98535573, 2.06533661, -2.06805411
])
assert (np.allclose(model.theta, theta))
g1 = fo_fc_cd(model.loglike, model.theta * 0.95)
g2 = model.gradient(model.theta * 0.95)
assert (np.allclose(g1, g2))
H1 = so_gc_cd(model.gradient, model.theta)
H2 = model.hessian(model.theta)
assert (np.allclose(H1, H2))
assert (model.optimizer.success == True)
assert ((np.abs(model.optimizer.grad) < 1e-5).all())
def test_gam():
n_obs = 10_000
X = pd.DataFrame(np.zeros((n_obs, 4)), columns=['x0', 'x1', 'x2', 'y'])
X['x0'] = rng.choice(np.arange(5), size=n_obs, p=np.ones(5) / 5)
X['x1'] = rng.uniform(-1, 1, size=n_obs)
X['x2'] = rng.uniform(-2, 2, size=n_obs)
u0 = dummy(X['x0']).dot(np.array([-0.2, 0.2, -0.2, 0.2, 0.0]))
f1 = (3.0 * X['x1']**3 - 2.43 * X['x1'])
f2 = (X['x2']**3 - X['x2']) / 10
eta = u0 + f1 + f2
mu = np.exp(eta) # mu = shape * scale
shape = mu * 2.0
X['y'] = rng.gamma(shape=shape, scale=1.0)
model = GAM("y~C(x0)+s(x1, kind='cr')+s(x2, kind='cr')",
X,
family=Gamma(link=LogLink))
model.fit()
assert (model.opt.success == True)
assert ((np.abs(model.opt.grad) < 1e-5).all())
theta = np.array([49.617907465401451, 407.234892157726449])
assert (np.allclose(np.exp(model.theta)[:-1], theta))
theta = model.theta.copy()
eps = np.finfo(float).eps**(1 / 4)
grad_close = np.allclose(model.gradient(theta),
fo_fc_cd(model.reml, theta),
atol=eps,
rtol=eps)
hess_close = np.allclose(model.hessian(theta),
so_gc_cd(model.gradient, theta),
atol=eps,
rtol=eps)
assert (grad_close)
assert (hess_close)
def test_glm():
seed = 1234
rng = np.random.default_rng(seed)
response_dists = ['Gaussian', 'Binomial', 'Poisson', 'Gamma', "InvGauss"]
n_obs, nx, r = 2000, 10, 0.5
n_true = nx // 4
R = vine_corr(nx, 10, seed=seed)
X = {}
X1 = exact_rmvnorm(R, n_obs, seed=seed)
X2 = exact_rmvnorm(R, n_obs, seed=seed)
X2 = X2 - np.min(X2, axis=0) + 0.1
X['Gaussian'] = X1.copy()
X['Binomial'] = X1.copy()
X['Poisson'] = X1.copy()
X['Gamma'] = X1.copy()
X['InvGauss'] = X2.copy()
beta = dict(Binomial=np.zeros(nx),
Poisson=np.zeros(nx),
Gamma=np.zeros(nx),
Gaussian=np.zeros(nx),
InvGauss=np.zeros(nx))
beta['Gaussian'][:n_true * 2] = np.concatenate(
(0.5 * np.ones(n_true), -0.5 * np.ones(n_true)))
beta['Binomial'][:n_true * 2] = np.concatenate(
(0.5 * np.ones(n_true), -0.5 * np.ones(n_true)))
beta['Poisson'][:n_true * 2] = np.concatenate(
(0.5 * np.ones(n_true), -0.5 * np.ones(n_true)))
beta['Gamma'][:n_true * 2] = np.concatenate(
(0.1 * np.ones(n_true), -0.1 * np.ones(n_true)))
beta['InvGauss'][:n_true * 2] = np.concatenate(
(0.1 * np.ones(n_true), 0.1 * np.ones(n_true)))
for dist in response_dists:
beta[dist] = beta[dist][rng.choice(nx, nx, replace=False)]
eta = {}
eta_var = {}
u_var = {}
u = {}
linpred = {}
for dist in response_dists:
eta[dist] = X[dist].dot(beta[dist])
eta_var[dist] = eta[dist].var()
u_var[dist] = np.sqrt(eta_var[dist] * (1.0 - r) / r)
u[dist] = rng.normal(0, u_var[dist], size=(n_obs))
linpred[dist] = u[dist] + eta[dist]
if dist in ['InvGauss']:
linpred[dist] -= linpred[dist].min()
linpred[dist] += 0.01
Y = {}
Y['Gaussian'] = IdentityLink().inv_link(linpred['Gaussian'])
Y['Binomial'] = rng.binomial(
n=10, p=LogitLink().inv_link(linpred['Binomial'])) / 10.0
Y['Poisson'] = rng.poisson(lam=LogLink().inv_link(linpred['Poisson']))
Y['Gamma'] = rng.gamma(shape=LogLink().inv_link(linpred['Gamma']),
scale=3.0)
Y['InvGauss'] = rng.wald(mean=PowerLink(-2).inv_link(eta['InvGauss']),
scale=2.0)
data = {}
formula = "y~" + "+".join([f"x{i}" for i in range(1, nx + 1)])
for dist in response_dists:
data[dist] = pd.DataFrame(np.hstack((X[dist], Y[dist].reshape(-1, 1))),
columns=[f'x{i}'
for i in range(1, nx + 1)] + ['y'])
models = {}
models['Gaussian'] = GLM(formula=formula,
data=data['Gaussian'],
fam=Gaussian(),
scale_estimator='NR')
models['Binomial'] = GLM(formula=formula,
data=data['Binomial'],
fam=Binomial(weights=np.ones(n_obs) * 10.0))
models['Poisson'] = GLM(formula=formula,
data=data['Poisson'],
fam=Poisson())
models['Gamma'] = GLM(formula=formula, data=data['Gamma'], fam=Gamma())
models['Gamma2'] = GLM(formula=formula,
data=data['Gamma'],
fam=Gamma(),
scale_estimator='NR')
models['InvGauss'] = GLM(formula=formula,
data=data['InvGauss'],
fam=InverseGaussian(),
scale_estimator='NR')
models['Gaussian'].fit()
models['Binomial'].fit()
models['Poisson'].fit()
models['Gamma'].fit()
models['Gamma2'].fit()
models['InvGauss'].fit()
grad_conv = {}
grad_conv["Gaussian"] = np.mean(models['Gaussian'].optimizer.grad**
2) < 1e-6
grad_conv["Binomial"] = np.mean(models['Binomial'].optimizer.grad**
2) < 1e-6
grad_conv["Poisson"] = np.mean(models['Poisson'].optimizer.grad**2) < 1e-6
grad_conv["Gamma"] = models['Gamma'].optimizer['|g|'][-1] < 1e-6
grad_conv["Gamma2"] = models['Gamma2'].optimizer['|g|'][-1] < 1e-6
grad_conv["InvGauss"] = np.mean(models['InvGauss'].optimizer.grad**
2) < 1e-6
assert (np.all(grad_conv.values()))
param_vals = {}
param_vals["Gaussian"] = np.array([
0.01677157, 0.01768816, 0.03232757, -0.50586418, 0.00538817,
0.01215466, 0.46273009, 0.03222982, 0.51013559, -0.00482659,
-0.44925714, -0.08297647
])
param_vals["Binomial"] = np.array([
-0.04811123, 0.34608258, 0.02748488, 0.02109192, -0.35403311,
0.37825192, -0.46275101, 0.00668586, 0.06837819, 0.00136615, 0.00321255
])
param_vals["Poisson"] = np.array([
0.78523498, -0.52630851, -0.0407732, 0.02971785, -0.03919242,
-0.01845692, 0.34397533, -0.55594235, 0.0257876, 0.42205263, 0.13051603
])
param_vals["Gamma"] = np.array([
0.33020564, -0.00496934, -0.01392126, 0.03581743, -0.01186388,
0.03645015, -0.00609281, -0.01056508, 0.00163984, -0.03324063,
-0.00937269
])
param_vals["Gamma2"] = np.array([
0.33020564, -0.00496934, -0.01392126, 0.03581743, -0.01186388,
0.03645015, -0.00609281, -0.01056508, 0.00163984, -0.03324063,
-0.00937269, 0.09260053
])
param_vals["InvGauss"] = np.array([
0.51658718, -0.03040851, 0.14254292, 0.10087636, 0.05071923,
-0.05297573, -0.04039982, -0.04293772, 0.1251764, -0.02370386,
0.01912702, -0.66386179
])
param_close = {}
grad_close = {}
hess_close = {}
for key in param_vals.keys():
m = models[key]
param_close[key] = np.allclose(param_vals[key], m.params)
x = m.params * 0.98
grad_close[key] = np.allclose(fo_fc_cd(m.loglike, x), m.gradient(x))
hess_close[key] = np.allclose(so_gc_cd(m.gradient, x), m.hessian(x))
assert (np.all(param_close.values()))
assert (np.all(grad_conv.values()))
assert (np.all(grad_close.values()))
assert (np.all(hess_close.values()))
